This case report examined the characteristics of body composition, sleep quality, and autonomic nerve activity in active older adults with a younger body age—calculated from age trends in body composition and basal metabolic rate. We selected two cases who had a metabolic age younger than their actual age. They had good sleep quality, low sleep quality scores, no sarco-penia, strong muscle and grip strength, and balanced autonomic nervous system activity. Fur-thermore, they were determined to be much younger than their actual age. They were compared with two other age- and sex-matched cases, who had poor sleep quality, unbalanced autonomic nervous system activity, and had a physical age closer to their actual age. Older adults with more muscle mass and higher basal metabolism were younger than their actual age, had a better sleep status, and a good balance of autonomic nervous activity in the exercise stimulation. They also had lower percentages of body- and visceral fat and higher body water. Meanwhile, the older adults with standard muscle mass and basal metabolic rate had a poor sleep status and no sympathetic hyperactivity in the exercise simulation.

This study aimed to verify the effects of an acute increase in blood pressure (BP) and/or renal sympathetic nerve activity (rSNA) on the renal excretion of sodium and water and its potential effect on the regulation of NHE3 activity. Uninephectomized anesthetized male Wistar rats were divided into three experimental groups: 1) Sham, the rats had their BP and rSNA recorded, and urine was collected for 2 h; 2) bicuculline (Bic) paraventricular nucleus of the hypothalamus (PVN), rat BP, and rSNA were recorded, and urine was collected for 1 h at baseline conditions and 1 h after bicuculline injection into the PVN; 3) RNS + Bicuculine injection into the PVN, BP, and rSNA were recorded, and urine was collected 1h after RNS and 1 h after bicuculline injection into the PVN. Renal nerve stimulation (RNS) decreased sodium and water excretion independent of changes in BP. However, after Bic in the PVN during RNS stimulation, BP and rSNA increased up to 30% and 60%, respectively, increasing diuresis (5-fold) and natriuresis (2.3-fold), accompanied by a significant reduction in the NHE3 activity independent of GFR changes. In conclusion, an acute increase in BP overcomes such effects, generating diuresis, natriuresis, and NHE3 activity inhibition.

Background: Heart rate variability is a non-invasive, measurable, and established autonomic nervous system test. Long-term COVID-19 sequelae are unclear; however, acute symptoms have been studied. Objectives: To determine autonomic cardiac differences between long COVID-19 patients and heathy controls and evaluate associations among symptoms, comorbidities, and laboratory findings. Methods: This single-center study included long COVID-19 patients and healthy controls. The heart rate variability (HRV), a quantitative marker of autonomic activity, was monitored for 24 h using an ambulatory electrocardiogram system. HRV indices were compared between case and control groups. Symptom frequency and inflammatory markers were evaluated. The significance level of 5% (p-value 0.05) was adopted. Results: A total of 47 long COVID-19 patients were compared to 42 healthy controls. Patients averaged 43.8 (SD14.8) years old, and 60.3% were female. In total, 52.5% of patients had moderate illness. Post-exercise dyspnea was most common (71.6%), and 53.2% lacked comorbidities. COVID-19 patients had 4 times more dyslipidemia. CNP, D-dimer, and CRP levels were elevated (p-values of 0.0098, 0.0023, and 0.0015, respectively). The control group had greater SDNN24 and SDANNI (OR = 0.98 (0.97 to 0.99; p = 0.01)). Increased low-frequency (LF) indices in COVID-19 patients (OR = 1.002 (1.0001 to 1.004; p = 0.030)) and high-frequency (HF) indices in the control group (OR = 0.987 (0.98 to 0.995; p = 0.001)) were also associated. Conclusions: Patients with long COVID-19 had lower HF values than healthy individuals. These variations are associated with increased parasympathetic activity, which may be related to long COVID-19 symptoms and inflammatory laboratory findings.

In Los Angeles, at one point, the Covid-19 testing positivity rate was 6.25%, or one in sixteen. This translates to, on average, one in sixteen specimens testing positive and the vast majority testing negative. Usually, we run sixteen tests on sixteen specimens to identify the positive one(s). This process can be time consuming and expensive. Since a group of negative specimens pooled together for testing will produce a negative result, one single test could potentially eliminate many specimens. Only when the pooled specimen tests positive do we need further testing to identify the positive one(s). Based on this concept, we designed a strategy that will identify the positive specimen(s) efficiently. Assuming one in sixteen specimens is positive, we find that only four tests are needed. Furthermore, we can run them simultaneously, saving both resources and time. Although, in the real world, we cannot make the assumption of only one positive specimen, the same strategy works with slight modification and proves to be much more efficient than the conventional testing. Our strategy returns an answer 48% of the time in four tests and one time cycle. Overall, the average number of tests is seven or eight depending on the follow-up testing, and the average time cycle is about one and a half.

The lateral pressure generated by liquefied soil on pile is a critical parameter in the analysis of soil-pile interaction in liquefaction-susceptible sites. Previous studies have shown that liquefied sand behaves like a non-Newton fluid, and its effect on piles has rate-dependent properties. In this study, a simplified pseudo-static method for liquefiable soil-pile interaction analysis is proposed by treating the liquefied soil as a thixotropic fluid, which considers the rate-dependent behavior. The viscous shear force generated by the relative movement between the viscous fluid (whose viscosity coefficient varies with excess pore pressure and shear strain rate) and the pile was assumed to be the lateral load on the pile. The results from the simplified analysis show that the distribution of bending moment is in good agreement with experiments data. Besides, the effects of various parameters, including relative density, thickness ratio of non-liquefiable layer to liquefiable layer, and frequency of input ground motion, on the pile-soil rate-dependent interaction were discussed in detail.

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were de-fined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, Heart Rate Variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.

Variable nitrogen(N) rate fertilization based on remote sensing is challenging for cotton production fields, but active crop canopy sensors (ACS) appear as an alternative to make this practical on farm since they can be used at night as well. The crop spatial variability in inherent in crop production in general, and not on-the-go solutions can be used with this type of active sensing technologies. Thus, the purpose of this study was to investigate the potential of two vegetation indices to identify the N requirement variability for cotton plants and to develop prototype algorithms for topdressing nitrogen variable rate on commercial and experimental areas, using the N-sufficiency methodology based on virtual reference. The concept of virtual reference is to use a histogram to characterize the vegetation index of properly fertilized plants without establishing an N-rich plot as a reference strip. The experiment was conducted in strips with four different N rates (0, 45, 90 and 180 kgN ha^-1) during the 2015, 2016, 2017 and 2018 crop seasons in partnership with large cotton producers in Mato Grosso and also in experimental area of Embrapa Agrosilvopastoral. Two algorithms for variable rate nitrogen fertilization for cotton were developed, namely: 1) N recommendation algorithm for cotton in commercial production system: N rate (kg.N ha^-1) = -234.79 ISN² + 49,879 ISN + 195.15; R² = 0.97; and 2) for cotton grown in experimental area: N dose (kgN ha^-1) = -174.73 ISN² - 107.21 ISN + 306.78; R² = 0.94.

Aedes (Stegomyia) albopictus is a mosquito native to Southeast Asia. Currently, it has a wide distribution in America where natural infection with arboviruses of medical and veterinary importance has been reported. In spite of their importance in the transmission of endemic arbovirus, basic information of parameters affecting their vectorial capacity is poorly investigated. The aim of the work was to update the distribution range of Ae. albopictus in the Americas, review the blood-feeding patterns and compare the minimum infection rate (MIR) of the dengue virus (DENV) between studies of vertical and horizontal transmission. The current distribution of Ae. albopictus encompasses 21 countries in the Americas. Extensive review has been conducted for the blood-feeding patterns of Ae. albopictus. The results suggest that the mosquito is capable of feeding on 16 species of mammals and five species of avian. Humans, dogs, and rats are the most common host. Eight arboviruses with the potential to infect humans and animals have been isolated in Ae. albopictus. In the United States of America (USA), Eastern equine encephalitis virus, Keystone virus, La Crosse Virus, West Nile virus, and Cache Valley virus were isolated in the Asian mosquito. In Brazil, Mexico, Colombia, and Costa Rica, DENV (all serotypes) has been frequently identified in field-caught Ae. albopictus. Overall, the estimated MIR in Ae. albopictus infected with DENV is similar between horizontal (10.95) and vertical transmission (8.28). However, in vertical transmission, there is a difference in the MIR values if the DENV is identified from larvae or adults (males and females emerged from a collection of eggs or larvae). MIR estimated from larvae is 14.04 and in adults is 4.04. In conclusion, it has to be highlighted that Ae. albopictus is an invasive mosquito with wide phenotypic plasticity to adapt to broad and new areas, it is highly efficient to transmit the DENV horizontally and vertically, it can participate in the inter-endemic transmission of the dengue disease, and it can spread zoonotic arboviruses across forest and urban settings.

The aim of the work was to update the distribution range of Aedes (Stegomyia) albopictus Skuse in the Americas, review the blood-feeding patterns and compare the minimum infection rate (MIR) of the dengue virus (DENV) between studies of vertical and horizontal transmission. The current dis-tribution of Ae. albopictus encompasses 21 countries in the Americas. Extensive review has been conducted for the blood-feeding patterns of Ae. albopictus. The results suggest that the mosquito is capable of feeding on 16 species of mammals and five species of avian. Humans, dogs, and rats are the most common host. Eight arboviruses with the potential to infect humans and animals have been isolated in Ae. albopictus. In the United States of America (USA), Eastern equine encephalitis virus, Keystone virus, La Crosse Virus, West Nile virus, and Cache Valley virus were isolated in the Asian mosquito. In Brazil, Mexico, Colombia, and Costa Rica, DENV (all serotypes) has been frequently identified in field-caught Ae. albopictus. Overall, the estimated MIR in Ae. albopictus infected with DENV is similar between horizontal (10.95) and vertical transmission (8.28). However, in vertical transmission, there is a difference in the MIR values if the DENV is identified from larvae or adults (males and females emerged from a collection of eggs or larvae). MIR es-timated from larvae is 14.04 and in adults is 4.04. In conclusion, it has to be highlighted that Ae. albopictus is an invasive mosquito with wide phenotypic plasticity to adapt to broad and new areas, it is highly efficient to transmit the DENV horizontally and vertically, it can participate in the inter-endemic transmission of the dengue disease, and it can spread zoonotic arboviruses across forest and urban settings.

Rural electrification rate (RER) in Africa is still low to date. Several countries in Sub-Saharan Africa have tried to address this problem using conventional single-phase two-wire or three-phase three-wire systems, however at large costs due to the nature of dispersed rural load centers, low load demand, and low population density. Another solution of off-grid generation creates associated health problems. Therefore, this paper undertakes a review of a single wire earth return (SWER) network as a RER improvement solution. The paper undertakes intensive literature review to elucidate challenges and solutions to the implementation of SWER technology. Advantages of SWER technology discussed make it the choice for RER improvement in Sub-Saharan African countries. After that, a case study is selected in rural Tanzania, and a preliminary SWER network design is undertaken.

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions of air pollutants, sensitivity experiments were implemented by random forest (RF) model, with the comparison of contributions of meteorology, road traffic, and emission sources between different periods. Besides, an emulator was operated to suggest an appropriate limit for control of transportation. The RF models showed different mechanisms for air pollutants. Within-city Migration index (WMI) was more important in the normal, pre-lockdown and post-pandemic model while Out-Migration Index (OMI) was emphasized in the lockdown model. In the COVID-19 lockdown period, 73.3% of the reduction can be attributed to the decreased road traffic, showing massive impact of road traffic on the air quality. In the post-pandemic period, meteorology controlled about 42.2% of the decrease and emissions from industry and household controlled 40.0% while road traffic only contributed to 17.8%. It was suggested that priority of restriction should be given to road traffic within the city. A limit of less than 40% on the control of the road traffic can get a better effect, especially for cities with severe traffic pollution.

Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small data is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNN), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data, and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the UCI-KDD EGG dataset. This article explains the step-wise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques, and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

Background: Since the previous study dealing with the case fatality ratio and infection fatality ratio caused by COVID-19, the author has received many comments that prompted the question: "Why did an optimistic prognosis fail?" To answer this question, a more detailed and expanded analysis was carried out in a new study. Objective: To evaluate the dynamics of monthly numbers of cases, deaths, tests and CFR worldwide during three phases of the COVID-19 pandemic. Material and Methods: Twenty three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, CFR, IFR, etc. were counted for various periods of time for each of the 213 countries, then results related to different periods of time were compared. Results: The analysis of the main epidemiological parameters led to the division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases, and mortality due to COVID-19. By the end of the second phase, the worldwide statistics indicated the imminent end of the pandemic, but the third phase was characterized by a sudden rise in the number of new cases and deaths that could not be explained rationally. The most dramatic evolution of the epidemic curve occurred in the countries where doctors had successfully battled COVID-19 during the first two phases of the pandemic. Conclusions: Despite the decrease in overall death numbers during the latest months analyzed, additional study is necessary to identify the cause for the increase in the number of new cases and deaths during the third phase of the pandemic. Only complete information regarding the positive and negative impact of medical and non-medical methods of diagnostics and prophylaxis of COVID-19 can help to organize effective measures to end the current pandemic and prevent a similar one from occurring in the future. Presumably, there are several causes of the negative evolution of the current pandemic, including the overreliance on PCR tests, application of non-specialized premises for quarantine and treatment, decrease in herd and individual immunity, inadequate change of therapeutic protocols, and ignoring prophylactic treatment. It can be suggested that the use of immunemodulatory drugs, for example, thymus extract or thymic peptides, in groups of people with compromised immunity is necessary, and prophylactic and therapeutic protocols should be changed from the 'standard' types to 'personalized' ones.

The closed forms of the non-resonant thermonuclear function in the Maxwell-Boltzmann and Tsallis case with depleted tail are obtained in generalized special functions. The results are written in terms of H-function of two variables. The importance of the results in this paper lies in the fact that the reaction rate probability integrals in Maxwell-Boltzmann and Tsallis cases are not obtained by the conventional method of approximation or by means of a single variable transform technique but by means of a two variable transform method. The Behaviour of the depleted non-resonant thermonuclear functions are examined using graphs. The results in the paper are of much interest to astrophysicists and statisticians in their further work in this area.

Previous studies investigated the effects of seed rates and seed recycling practices on the yield and yield related variables. However, higher yield does not always guarantee cost-efficiency. This study aimed at investigating the yield effects of plot-level seed rate along with the cost-benefit analysis of seed recycling practices. This study has introduced the dose-response model to the existing analytical methods used in analyzing the effect of different agrochemicals on crop yield. A multi-stage stratified sampling technique was used to select a total of 450 sample respondents. Data was gathered using a mix of data collection tools. Descriptive statistics along with the dose-response model have been applied for data analysis. Farmers of the study were found to be dissimilar in terms of their seed rate application. The dose-response analysis indicated that the highest average wheat yield has been associated with a seed rate of 50 kg ha-1 above what is recommended. The yield effect of seed recycling has also been assessed and a one-time seed recycling has caused a yield decline of 665 kg ha-1 as compared to the non-recycled seeds. The cost reduced by using recycled seed is by far lower than the economic gains associated with using unrecycled and fresh seeds. The cost-benefit analysis made clear that farmers can reduce their seed costs through seed recycling but their yields and net income can be best improved by using unrecycled CBWS. Thus, farmers must be encouraged to make use of unrecycled seed by establishing agricultural credit schemes geared towards seed procurement and seed price subsidy as key strategies to reduce economically wasteful seed recycling practices.

This work assesses sea-level rise using three different models created on Free and Open-Source Software for Geographic Information System (FOSS4GIS). Based on regional projections of Special Report on Climate Change and Oceans and Cryosphere (SROCC) of the Intergovernmental Panel on Climate Change (IPCC), the models were applied to a case of study on Rio Grande do Sul coast – Brazil under different sea-level rise scenarios by the end of this century. The End Point Rate for QGIS (EPR4Q), calculates a shoreline projection using End Point Rate method. The Uncertainty Bathtub Model (uBTM), analyses the sea-level rise impact by the uncertainty of sea-level projec-tions and vertical error of the Digital Elevation/Terrain Model (DEM/DTM). The Bruun Rule for Google Earth Engine Model (BRGM) predicts the shoreline position with sea-level rise, using topographic and bathymetric data from Unmanned Aerial Vehicles (UAV) and Coastal Modelling System (SMC – Brazil), respectively. The results indicated a maximum shoreline retreat for 2100 of -502 m and -1727 m using EPR4Q and BRGM, correspondingly. The uBTM using the land-use of Mapbiomas showed a maximum of 44.57 km2 of urban area impacted by the sea-level flood. This research highlights the possibility of performing coastal management analysis in GIS environ-ment using non-commercial software.

Wenchuan earthquake that occurred in China in 2008 caused severe damage to a large number of electric substations. In this paper, Kriging interpolation method was used to calculate the impact area of the instrumental seismic intensity in Wenchuan earthquake, and to compare the intensities based on strong motion observation against the instrumental seismic intensities at locations where the observation data is available. The instrumental seismic intensities were calculated for the Wenchuan Earthquake at substations in the national power grid with voltage of 110kV or higher in areas of Mianyang, Deyang, Guangyuan and Chengdu. The cumulative Gaussian distribution function was then used to fit the relationship of the curves of the damage probabilities of high-voltage electrical equipment such as transformers, voltage mutual inductors, current mutual inductors, circuit breakers, isolating switches and lightning arrester with their instrumental seismic intensities. The damage probability density distribution curve of high-voltage electrical equipment based on the instrumental seismic intensities was obtained. The results showed that: (1) In the lower seismic intensity region, the mean instrumental seismic intensity was in good agreement with the traditional seismic intensity, but there was noticeable dispersion; in regions of intensity IX and above, the instrumental intensity was lower than the seismic intensity, but there was a lower degree of dispersion. (2) Among high-voltage electrical equipment, the transformers were most vulnerable to damage and they had some damage even under lower instrumental intensity. More damage would be produced when the instrumental intensity reached VIII or above; the second most vulnerable equipment was the circuit breaker, and the damage was most likely to occur when the instrument intensity was IX or above . (3) The damage rate curves of lightning arresters, current mutual inductors, voltage mutual inductors and isolating switches were relatively close to each other and the damage probability was the highest when the instrumental intensity was about X.

Wenchuan earthquake that occurred in China in 2008 caused severe damage to a large number of electric substations. In this paper, Kriging interpolation method was used to calculate the impact area of the instrumental seismic intensity in Wenchuan earthquake, and to compare the intensities based on strong motion observation against the instrumental seismic intensities at locations where the observation data is available. The instrumental seismic intensities were calculated for the Wenchuan Earthquake at substations in the national power grid with voltage of 110kV or higher in areas of Mianyang, Deyang, Guangyuan and Chengdu. The cumulative Gaussian distribution function was then used to fit the relationship of the curves of the damage probabilities of high-voltage electrical equipment such as transformers, voltage mutual inductors, current mutual inductors, circuit breakers, isolating switches and lightning arrester with their instrumental seismic intensities. The damage probability density distribution curve of high-voltage electrical equipment based on the instrumental seismic intensities was obtained. The results showed that: (1) In the lower seismic intensity region, the mean instrumental seismic intensity was in good agreement with the traditional seismic intensity, but there was noticeable dispersion; in regions of intensity IX and above, the instrumental intensity was lower than the seismic intensity, but there was a lower degree of dispersion. (2) Among high-voltage electrical equipment, the transformers were most vulnerable to damage and they had some damage even under lower instrumental intensity. More damage would be produced when the instrumental intensity reached VIII or above; the second most vulnerable equipment was the circuit breaker, and the damage was most likely to occur when the instrument intensity was IX or above . (3) The damage rate curves of lightning arresters, current mutual inductors, voltage mutual inductors and isolating switches were relatively close to each other and the damage probability was the highest when the instrumental intensity was about X.

It is unclear why European countries have been more severely affected by COVID-19 than East Asian countries. In this ecological study we compared the COVID-19 epidemics (cumulative incidence and mortality rates), host genetic susceptibility and national responses (testing intensity) in all European versus all Western Pacific/East Asian countries reporting cases in the first month of the epidemic. The host-genetic-susceptibility assessment was limited to the frequency of the D-allele of the angiotensin converting enzyme-1 (ACE-1) which has been found to be positively associated with COVID-19 incidence within European countries. Despite earlier epidemics, countries from the Western Pacific reported lower cumulative numbers of COVID-19 cases/100 000 inhabitants than European countries (P=0.0002). The Western Pacific countries also reported fewer cumulative COVID-19 deaths/100 000 (P=0.0024). Whilst there was little difference in the cumulative number of tests conducted/100 000, the percent of COVID-19 tests reported positive was higher in Europe than the Western Pacific (P=0.0076). The frequency of the ACE-1 D-allele was lower in the Western Pacific than European countries (P=0.0007). Our results suggest that a combination of different testing strategies and host genetic susceptibility contribute to difference in severity of the COVID-19 epidemics in the Western Pacific/East Asia and Europe.

Different cohort studies documented a lower glomerular filtration rate (GFR) in former extremely low birth weight (ELBW, <1000 g) neonates throughout childhood when compared to term controls. The current aim is to pool these studies to describe the GFR pattern over the pediatric age range. To do so, we conducted a systematic review on studies reporting on GFR measurements in former ELBW cases while GFR data of healthy age-matched controls included in these studies were co-collected. Based on 248 hits, 6 case-control and 3 cohort studies were identified, with 444 GFR measurements in 380 former ELBW cases (median age 5.3-20.7 years). The majority were small (17-78 cases) single center studies, with heterogeneity in GFR measurement (inulin, Cystatin C or creatinine estimated GFR formulae) tools. Despite this, the median GFR (ml/kg/1.73m²) within case-control studies was consistently lower (-13, range -8 to -25%) in cases, so that a relevant minority (15-30%) has a eGFR<90 mgl/kg/1.73m²). Consequently, this pooled analysis describes a consistent pattern of reduced eGFR in former ELBW cases throughout childhood. Research should focus on perinatal risk factors for impaired GFR and long-term outcome, but is hampered by single center cohorts, study size, and heterogeneity of GFR assessment tools.

The recycling, incineration, and final disposal rate of municipal solid waste (MSW) are calculated based on the total amount of waste input to each facility in many countries. These statistic data have serious limitation in setting the national goal and policy for effective waste management because it is not considering the amount of foreign objectives in the process of each life-cycle stage. This case study is to estimate the actual rates of recycling, incineration, and final disposal by material flow analysis (MFA) after the collection of MSW in Korea. The actual rates of recycling, incineration and final disposal for MSW in 2016 were 49.9%, 32.9% and 23.1% respectively, indicating that the recycling rate was lower by 10.1%, while the incineration and final disposal rates were raised by 7.6% and 8.4% respectively, compared with the statistics for current MSW. In addition, the changed actual rates of recycling, incineration treatment, and final landfill, and variation of waste treatment charge according to treated amounts per treatment method was analyzed. This results of this study will contribute to establish national level of plan on effective waste management.

The mantle mass flow interconnected with the process of Glacial Isostatic Adjustment (GIA) and the reformation of the Earth’s crust constantly perturbs the observed gravity field towards a hypothetic isostatic state. We analyse the temporal changes of the gravity field from the GRACE data, using different mathematical and/or statistical methods to detect the GIA amidst other gravity signals. A number of gravimetric post-glacial land uplift rate (LUR) modelling methods are investigated and compared with the data from a total number of 515 GPS stations and preferred GIA forward models in Fennoscandia and North America. We investigate three mathematical methods, namely regression, principal component, and independent component analysis (ICA) to extracting the GIA signal from the GRACE monthly geoid heights. We use some regularization techniques to exploit the GRACE monthly data to their maximum spatial resolution and to increase the Signal to Noise Ratio of their short wavelengths. Near the centres of the study areas the gravimetric LUR model using the fast-ICA algorithm of Hyvärinen and Oja (2000) is shown to be in a complete agreement with the GPS data and the predictions of the GIA forward models, and for the whole areas, subject to epeirogeny movement of the two regions, their discrepancies reach to the extrema at -1.8 and +3.3, and -4.5 and +7.5 mm/a, respectively. We show that the largest discrepancies between the gravimetric model using the ICA method and the GIA forward model, occur for the sub-regions likely collocated with strong ice mass change signals.

Tissue engineering is an emerging field of research due to its growing impact on the regeneration of tissue injured from damage and organ failure. Small hydrogel microspheres containing regenerative cells and stimulators can be used as building blocks to regenerate tissue by injecting them into damaged areas; a microneedle is used to minimize the incision and any associated infections. To address the need for production of a large number of microspheres for use in tissue engineering, we developed a 3D coaxial multi-nozzle flow-focusing device fabricated via a simple 3D-printing method that demonstrates a high rate of microsphere production per nozzle. This device has six coaxial microscale nozzles that produce hydrogel microspheres. Two individual parts—the inlet and nozzle—can be made separately by a 3D printer and bonded together using uncured photo-curable resin as glue. The dimensions of the microspheres are between 100 μm and 1200 μm. They are produced by adjusting the flow rate ratio between the dispersed and continuous media. A flow rate ratio of 180 demonstrated the highest microsphere production rate of 2.12e+5 microspheres per sec (0.25 mL/min). This microsphere production rate per nozzle is four times higher than that of currently available devices.

The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.

The livestock sector can be a major contributor to the mitigation of greenhouse (GHG) emissions. Within the sector, beef production produces the largest proportion of the livestock sector’s direct emissions. The objective of this study was to assess the on-farm GHG emissions in semi-arid rangelands in Argentina and to identify the relationships between emissions and current farm management practices. A survey recorded detailed information on farm management and characteristics. Assessments of GHG emissions were based on the IPCC Tier 2 protocols [1]. The relationships between farm management and GHG emissions were identified using General Linear Models. Cluster analysis was used to identify groups of farms that differed from others in emissions and farm characteristics. Emissions per product sold were low on farms that had improved livestock care management, rotational grazing, received technical advice, and had high animal and land productivities. Emissions per hectare of farmland were low on farms that had low stocking rates, low number of grazing paddocks, little or no land dedicated to improved pastures and forage crops, and low land productivity. Our results suggest that the implementation of realistic, relatively easy-to-adopt farming management practices has considerable potential for mitigating GHG emissions in semi-arid rangelands of central Argentina.

The use of machine learning techniques in predictive health care is on the rise with minimal data used for training machine-learning models to derive high accuracy predictions. In this paper, we propose such a system, which utilizes Heart Rate Variability (HRV) as features for training machine learning models. This paper further benchmarks the usefulness of HRV as features calculated from basic heart-rate data using a window shifting method. The benchmarking has been conducted using different machine-learning classifiers such as artificial neural network, decision tree, k-nearest neighbour and naive bays classifier. Empirical results using MIT-BIH Arrhythmia database shows that the proposed system can be used for highly efficient predictability of abnormality in heartbeat data series.

Visible Light Communication (VLC) uses an intensity-modulation and direct-detection (IM/DD) scheme to transmit data. The light source utilized in VLC structures is constantly switched on and off rapidly, resulting in flickering. Furthermore, most of illumination systems exclude dimming support to permit customers to dim the light source to the desired stage. Therefore, the modulation scheme for information transmission in VLC should consist of flicker mitigation and dimming manage abilities. Double inverse pulse position modulation (DIPPM), sub-carrier pulse position modulation (SC-L-PPM), and M-ary variable pulse position modulation (M-VPPM) are recommended for minimizing the flicker issues and supporting an excessive degree of dimming for VLC systems. In this paper, a comparison is introduced between DIPPM, SC-L-PPM, and M-VPPM according to error performance and bit rate. In addition, a simulation is carried out to measure the optical power distribution for a LED lamp in an indoor room topology for each modulation technique. The obtained results indicated that SC-L-PPM is the best choice regarding the bit error rate (BER) and optical power distribution compared to the other two schemes. A 10^-6 BER is achieved with a very low power requirement at L=8, while a remarkable power distribution of 1.5 to 6.5 dBm is observed from 3×5×5 m³ room corners to the center, respectively. According to the bit rate, it is noticed that, M-VPPM is the most efficient one compared to the two other schemes. It achieves 2.3×10⁷ bps at signal to noise ratio (SNR) of 22 dB and M=8.

Field experiments were carried out during 2011-2014 at the Research and Experimental Center to Combating Desertification located in the Elsen Tasarkhai station of central Mongolia. The study was aimed to identify the effects of oxidized brown coal humic acid fertilizer on the relative growth rate of several trees for forest rehabilitation. The trees used were Populus sibirica, Salix ledebouriana and Acer tataricum. The experiment was carried out with the concentrations of 2,000, 10,000 and 20,000 mg L-1 of humic acid fertilization treatments. The measurement of relative height growth rate (RHGR) was conducted for a period of 4 years. The treatments showed significant differences within humic fertilizer concentrations which differed depending on the species. Compared to the monthly RHGR over the study, the treatment of fertilizers were significantly better for tree growth. P. sibirica in the 10,000 and 20,000 mg L-1 humic acid fertilizers had significant height growth rates. In addition, when the humic acid treatments were compared to control, A. tataricum decreased over the years which were statistically significant for high growth rate and a positive effect of humic acid fertilization treatments was observed. Furthermore, results showed that oxidized brown coal humic acid fertilizers as organic fertilizer can have a positive effect for the growth of A. tataricum during the study years. The results showed that the soil chemical properties EC, CO2, NO3, and K2O were significant among all the treatments compared to the control. The effect on P2O5 statistically significantly increased among all the treatments. However, pH and Mg were not significant effect among all the treatments. Combining the results obtained from the oxidized brown coal humic acid fertilization with sustainable land management practices can help improve soil organics for environmental issues in degraded sandy soil regions.

In this study, we applied a central composite design to estimate independent variables and establish optimal conditions of filtration rate and feces production that enhance filtration of suspended organic matter by the freshwater mussels Sinanodonta woodiana. The results indicated that statistical design methodology offers an efficient and feasible approach for high filtration and low feces production condition optimization. The proposed model equation takes into account the quantitative effect of variables and also the influence of interactions among variables on mussel filtration rate. Under the optimal experimental conditions (mussel size, 13.0 ± 0.2 cm; water current, 17.5 L/h), the experimental filtration rate of 4.47 ± 1.82 L/mussel/h showed a degree of correspondence with the predicted value of 8.4 L/mussel/h, which verified the practicability of this optimum strategy.

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

The considerable biological and medicinal activities of pyridazines has stimulated considerable research on efficient syntheses of these derivatives. In the last decade, microwave irradiation has generally been used for the energy source. As demonstrated in recent studies, pressure reactor “Q-tubes” may be used to accelerate several of these reactions in a more optimal and safer manner (compared to microwaves). In these studies there has been postulated a pathway for the formation of pyridazino[5,4,3-de][1,6]naphthyridine derivatives . In this paper we consider this pathway, and an alternate pathway, for several reactions. Contrary to the suggestion in these studies the pathway in which initial dimerization of malononitrile was postulated could be excluded based on chemical evidence. The reactions performed were the reaction of arylhydrazonals 1a,b with benzylidinemalononitrile which afforded in Q-tube the 3-acyl-4-aryl-1-phenyl-6-amino-1,4-dihydropyridazines, and the reaction of arylhydrazonals 1a,b, malononitrile 9 and aromatic aldehydes 10a-g in Q-tubes which afforded the tricyclic systems 12a-n whose structure could be established by X-ray crystal structure determination. In conclusion, we have added to the work of the recent studies by excluding a reaction pathway for one of their reaction products.

Thin films (100-400 nm) of Ba_xSr_1-xTiO₃ (0≤x≤1) deposited in RF-magnetron co-sputtering equipment are presented in this research work. The change of deposition rate, gap energy, and resistivity as a function of temperature- applied power change in the growth parameters was studied through the ISO colour-code lines constructed with MATLAB: By analysing the trend information and take into account the influence of the calculated "x" parameter with the Boltzmann profile fitting is proposed a method to allow a controlled set up of the RF-magnetron co-sputtering system and predict the E_g and resistivity values in the Ba_xSr_1-xTiO₃ solid solution with 0≤x≤1 for amorphous and crystalline phases. Also, a versatile tool to optimise the deposition process and material properties.

Remote sensing technology is crucial for accurate rooftop detection, benefiting urban planning, disaster management, and solar resource estimation. This study employs Efficient Interactive Segmentation (EISEG) to enhance the efficiency of remote sensing image labeling, with a particular focus on rooftop detection. It is necessary to use modern technology because traditional manual labelling methods are labor-intensive and complicated. The study introduces a novel framework on deep learning semantic segmentation models, facilitating an efficient approach to rooftop identification using high-resolution UAV remote sensing datasets. Large dataset of labeled UAV rooftop building images, in which each superpixel region is assigned a binary label indicating rooftop presence. Advanced methods including Asymmetric Neural Network (ANN), Dual At-tention Network (DANet), PP-LiteSeg, and Deeplab3 are implemented for automatic rooftop detection due to their higher performance and advanced architectures. These models are executed on the Baidu deep learning platform PaddlePaddle, generating initial rooftop segmentation maps crucial for estimating photovoltaic resources. The ANN model emerges with the highest accuracy at 96%, followed by DANet at 95.09%, PP-LiteSeg at 94.54%, and Deeplab3 at 81.61%. The outcomes presenting efficient models for automated rooftop identification, and demonstrating the continuous need for improving deep learning techniques in smart and sustainable cities.

Nuclear waste is one of the most important environmental problems of nuclear power plants. A novel renewable distillation method has been proposed for an on-site recycling of the spent nuclear fuel and separation of its valuable components from the fissile isotopes, which is especially applicable for reactors using liquid fuels.

Efficient beam steerable high-gain antennas enable high-speed data rates over long-distance networks, including wireless backhaul, satellite communications (SATCOM), and SATCOM On-the-Move. These characteristics are essential for advancing contemporary wireless communication networks, particularly within 5G and beyond. Various beam-steering solutions have been proposed in the literature, with passive beam-steering mechanisms employing planar metasurfaces emerging as cost-effective, power-efficient, and compact options. These attributes make them well-suited for use in confined spaces, large-scale production and widespread distribution to meet the demands of the mass market. Utilizing a dual-band antenna terminal setup is often advantageous for full duplex communication in wireless systems. Therefore, this article presents a comprehensive review of the dual-band beam steering techniques for enabling full-duplex communication in modern wireless systems, highlighting their design methodologies, scanning mechanisms, physical characteristics, and constraints. Despite the advantages of planar metasurface-based beam steering solutions, literature on dual-band beam steering antennas supporting full duplex communication is limited. This review article identifies research gaps and outlines future directions for developing economically feasible passive dual-band beam-steering solutions for mass deployment.

The detection and recognition of tables in image-based documents is a challenging and essential task for automated data extraction and anal- ysis. This paper introduces a methodology that utilizes Convolutional Neural Networks (CNNs) to address this problem. By harnessing the ro- bust visual recognition abilities of CNNs, the proposed method effectively detects and extracts tables from image-based documents. Additionally, an Encoder-Dual Decoder (EDD) architecture is employed to extract the textual content from table cells, enabling the transformation of visual data into structured, machine-readable formats. This structured data is then further processed using a Vector Space Model (VSM)-based language modeling technique for query-based table retrieval. The methodology’s accuracy is assessed by analyzing the spatial relationships between the extracted cells and comparing them against a pre-defined table structure. The results provide an evidence to the effectiveness of the proposed ap- proach, with an impressive 85% accuracy rate and a corresponding error rate of 15%. This accurate extraction and recognition of tables from image-based documents can significantly enhance applications in data analysis, information retrieval, and knowledge extraction.

Building a new-type power system is the key core of the green and low-carbon development of energy, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy-energy-environment (3E) system. This paper constructs a "Dual-Triangle" theoretical framework consisting of the “Energy Trilemma” and the "Sustainable Development Triangle", by combining historical statistical analysis with scenario predictive analysis, analyzes the historical characteristics of the "Dual Triangle" and future development trends under different scenarios, and studies the coupled interaction relationship of the "Dual-Triangle" to explore the optimal transformation path of the power system that is most beneficial to the coordinated development of the 3E system. The conclusions are as follows: (1) The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, the level of coordinated development of the 3E system fluctuated upwards, and the operation status of the "Dual Triangle" was stable. (2) Positive coupling relationship between "Dual Triangle" of scenario S1 is close, which can be regarded as the preferred path for the reconstruction of the power system. Due to phased development has clear development priorities, scenario S2 will artificially break the balance of the “Energy Trilemma”, which is a development model that dynamically mitigates the challenges of the “Energy Trilemma”.(3)The contradiction between economy and cleanliness is continuously weakened, and security has become the core contradiction of the "Impossible Triangle". The combination of renewable energy and energy storage is the key to resolving this contradiction. This paper conducts an integrated and innovative application of the "Dual-Triangle" theory, and the results provide a case basis for how to achieve the coupled interaction of the "Dual-Triangle", promote high-quality energy development, and support the sustainable development of the economy and society.

Models of subsurface structures are important for successful deposit exploration but are challenged by the need to integrate data from different geophysical methods. In the present study, we evaluated a method of joint inversion in which audio-magnetotelluric (AMT) and dual frequency induced polarization (DFIP) data sets are inverted simultaneously to produce a consistent 2D resistivity model to show a clear image of subsurface structures. To achieve the objectives, we conducted AMT and DFIP surveys first on the same survey line within the Dongjun lead-zinc deposit in inner Mongolia by developing 31 AMT survey sites with an interstation separation of 40 m on a 1440 m survey track and operated in fifty-three frequencies in the range 1–10,400 Hz to record the resistivity distribution of subsurface to depths exceeding 800 m. The same survey setup up was applied to the DFIP method by utilizing the SQ-3C model with a pole-dipole array configuration and conducted measurements at frequencies of 4 Hz and 4/13 Hz. We first separately inverted AMT and DFIP measurements. The two-dimensional (2D) model obtained from AMT data revealed distinct low resistivity anomalies in the middle of the 2D inversion model. In contrast, the DFIP inversion model showed a high resistive body in the same region. In response to the discrepancies observed in the separate 2D inversion models, we implemented a joint inversion for both the AMT and DFIP datasets. The joint inversion resistivity model shows surficial conducting bodies and a high conductive body along the profile with relatively high Percent Frequency Effect (PEF) indicating high chargeability. The final joint inversion resistivity model clearly images the large silica alteration zone and the Pb-Zn mineralization. This study demonstrates the feasibility of a joint inversion methodology and highlights the value of integrating geophysical methods through joint inversion for enhanced characterization and exploration of lead-zinc ores.

Cervical cancer, primarily caused by high-risk human papillomavirus (HR-HPV) types 16 and 18, is a major global health concern. Persistent HR-HPV infection can progress from reversible pre-cancerous lesions to invasive cervical cancer, which is driven by the oncogenic activity of human papillomavirus (HPV) genes, particularly E6 and E7. Traditional screening methods, including cytology and HPV testing, have limited sensitivity and specificity. This review explores the ap-plication of p16/Ki-67 dual-staining cytology for cervical cancer screening. This advanced im-munocytochemical method allows for simultaneously detecting p16 and Ki-67 proteins within cervical epithelial cells, offering a more specific approach for triaging HPV-positive women. Dual staining and traditional methods are compared, demonstrating their high sensitivity and negative predictive value but low specificity. The increased sensitivity of dual staining results in higher detection rates of CIN2+ lesions, which is crucial for preventing cervical cancer progression. However, its low specificity may lead to increased false-positive results and unnecessary biopsies. The implications of integrating dual staining into contemporary screening strategies, particularly considering the evolving landscape of HPV vaccination and changes in HPV genotype prevalence are also discussed. New guidelines and further research are necessary to elucidate the long-term effects of integrating dual staining into screening protocols.

The rapid, sensitive, and selective detection of pathogenic bacteria is of utmost importance in ensuring food safety and preventing the spread of infectious diseases. Here, we present a novel, reusable, and cost-effective impedimetric sensor based on a dual bacteria-imprinted polymer (DBIP) for the specific detection of Escherichia coli O157:H7 and Staphylococcus aureus. The DBIP sensor stands out with its remarkably short fabrication time of just 20 minutes, achieved through the efficient electro-polymerization of o-phenylenediamine monomer in the presence of dual bacterial templates, followed by in-situ template removal. The key structural feature of the DBIP sensor lies in the cavity-free imprinting sites, indicative of a thin layer of bacterial surface imprinting. This facilitates rapid rebinding of the target bacteria within a mere 15 minutes, while the sensing interface regenerates in just 10 minutes, enhancing the sensor's overall efficiency. A notable advantage of the DBIP sensor is its exceptional selectivity, capable of distinguishing the target bacteria from closely related bacterial strains, including different serotypes. Moreover, the sensor exhibits high sensitivity, showcasing a low detection limit of approximately 9 CFU mL-1. The sensor's reusability further enhances its cost-effectiveness, reducing the need for frequent sensor replacements. The practicality of the DBIP sensor was demonstrated in the analysis of real apple juice samples, yielding good recoveries. The integration of quick fabrication, high selectivity, rapid response, sensitivity, and reusability makes the DBIP sensor a promising solution for monitoring pathogenic bacteria, playing a crucial role in ensuring food safety and safeguarding public health.

In IP networks, packet forwarding is destination-based and hop-by-hop, and routes are built as needed. Kwong et al. introduced a protection routing in which packet delivery to the destination node can proceed uninterrupted in the event of any single node or link failure. He then shows that “whether there is a protection routing to the destination” is NP-complete. Tapolcai find that two completely independent spanning trees, abbreviated as CISTs, can be used to configure the protection routing. In this paper, we propose dual protection routing trees, denoted as dual-PRTs to replace CISTs, which are less restrictive than CISTs. Next, we propose a transformation algorithm that uses dual-PRTs to configure the protection routing. Taking complete graphs Kn, complete bipartite graphs Km,n, hypercubes Qn, locally twisted cubes LTQn as examples, we provide a recursive method to construct dual-PRTs on them. This article will show that there are no two CISTs on K3,3, Q3, and LTQ3, but there exist dual-PRTs that can be used to configure the protection routing. As shown in the performance evaluation of simulation results, for both Qn and LTQn, we get the average path length of protection routing configured by dual-PRTs is shorter than that by two CISTs.

Background: Diagnosis of acute kidney injury (AKI) relies on serum creatinine (SCr) changes. This study investigated if urinary aminopeptidases are early and predictive biomarkers of cardiac surgery associated-AKI (CSA-AKI). Methods: Glutamyl aminopeptidase (GluAp), alanyl aminopeptidase (AlaAp), dipeptidyl peptidase-4 (DPP4), proteinuria, albuminuria, N-acetyl-β-D-glucosaminidase (NAG) and neutrophile gelatinase-associated lipocalin (NGAL) were measured in urine samples from 44 patients at arrival in Intensity Care Unit (ICU) after cardiac surgery. Sensitivity, specificity, positive and negative predictive value for diagnosis of stages 1, 2 and 3 of AKI were analyzed for the highest quartile of each marker. We also studied the relationship with SCr after surgery, 6- and 12-months glomerular filtration rate (GFR) and other long-term events over the next 5 years. Results: GluAp diagnosed the maximal number of patients that developed stage 2 or 3 of AKI, increasing diagnostic sensitivity from 0 to 75 %. Besides, GluAp and DPP4 was related to the decrease in GFR at 6- or 12-months after surgery. Conclusions: Urinary aminopeptidases are a potential tool for the early diagnosis of CSA-AKI, being GluAp the most effective marker for diagnosing stages 2 or 3 of AKI at ICU admission. GluAp and DPP4 serve as predictive biomarkers for the decrease in GFR.

Cancer patients, susceptible to severe COVID-19 outcomes, exhibit varied vaccine responses, particularly those who are receiving chemotherapy. This prospective cohort study enrolled 115 cancer patients, and data from 91 patients who received two doses of COVID-19 vaccines were analyzed. Blood was drawn at baseline, day 28, and 6 months post-second dose for neutralizing antibody analysis. The primary outcome was seroconversion rate against wild type and Omicron at day 28. Secondary outcomes included seroconversion at 6 months, factors associated with seroconversion, and safety. Of the 157 cancer patients screened, 91 were enrolled, with 45% receiving chemotherapy. Seroconversion rates at day 28 were 77% for the wild type and 62% for Omicron. Chemotherapy did not affect seroconversion (p=0.789 for wild type, p=0.597 for Omicron). Vaccine type positively correlated with seroconversion, with an adjusted ORR of 25.86 (p=0.029) for the wild type and 17.38 (p<0.001) for Omicron with the primary heterologous vaccine. Adverse events were grade 1 in 34.0% and grade 2 in 19.7% of participants. Despite lower seroconversion against Omicron, no difference was noted between chemotherapy and non-chemotherapy groups. COVID-19 vaccinations demonstrated good tolerability. This data underscores vaccine efficacy considerations in cancer patients, informing tailored strategies for this vulnerable population.

The NW-SE trending dextral strike-slip faults on the north side of the Tian Shan play an important role in accommodating the crustal shortening, which mainly includes the Karatau fault, Talas-Fergana fault, Dzhalair-Naiman fault, Aktas fault, Dzhungarian fault (also named Bolokenu-Aqikekuduk fault) and Chingiz fault. The classic viewpoint is that the strike-slip fault is the adjustment product caused by the difference of the crustal shortening from west to east. Another viewpoint attributes the dextral strike-slip fault to large-scale sinistral shearing. The Alakol Lake Fault is situated along the northern margin of the Dzhungarian Gate, stretching for roughly 150 km from Lake Ebinur to Lake Alakol. Our team utilized aerial photographs, Satellite Stereo-imagery, and field observation to map the distribution of the Alakol Lake fault. Our findings provided evidence supporting the assertion that the fault is a dextral strike-slip fault. In reference to its spatial distribution, Lake Alakol is situated in a pull-apart basin that lies between two major dextral strike-slip fault lines: the Chingiz and Dzhungarian faults. The Alakol Lake Fault serves as a connecting point for these two faults, resulting in the formation of a mega NW-SE dextral strike-slip fault zone. According to our analysis of the dating samples taken from the alluvial fan, as well as our measurement of the displacement of the riser and gully, it appears that the Alakol Lake fault has a dextral strike-slip rate of 0.8-1.5mm/yr. The strike-slip rate of the Alakol Lake fault is comparatively higher than that of the Chingiz fault in the northern region (~0.7 mm/yr), but slower than that of the Dzhungarian fault in the southern (3.2-5 mm/yr). The Chingiz-Alakol-Dzhungarian fault zone shows a gradual decrease in deformation towards the interior of the Kazakhstan platform. We contend that the rate of deformation observed is better explained by the crustal shortening adjustment model, rather than the large-scale sinistral shearing model.

Accurate estimation of livestock carrying capacity (LCC) and implementing an appropriate actual stocking rate (ASR) is key to sustainable management of grazing adapted alpine grassland ecosystems. Reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here we demonstrate limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in efforts to support more accurate and reliable estimation of LCC and ASR. Results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. Findings from a smaller-scale study that incorporate phenology timings into estimation of annual aboveground biomass reveal that Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000-2005. The methods proposed can be extended to map grassland grazing pressure by predicting LCC and tracking ASR, thereby improving sustainable resource use in alpine grasslands.

Yellow eels were sampled by electrofishing in 1979, 1980 and 1981 in Vester Vedsted Baeck, Denmark, stem 6 km, catchment 24 km², with outlet to the North Sea. Yellow eels were aged by burning the otoliths. The eels were not sexed, and they varied from 6.5 to 44 cm in length. The ages varied from 0+ to 10+ year. The annual growth rate ∆ varied from 3.4 cm for 0+ eel to 2.2 cm for 10+ eel with a mean of 3.1 cm. Body mass wet wt. was recalculated to Kcal energy, with an annual growth rate ∆ of 5.33 Kcal. In contrast to body mass, the annual growth rate ∆ in Kcal increased with age. Von Bertalanffy growth trajectory (cm) of length-at-age was calculated and L∞ = 118.4 cm. Annual natural mortality M was estimated, and M was significantly depending on body mass, i.e. high M and low body mass vs. low M at high body mass. The biological production was calculated to 13.5 g wet weight m^-2 year^-1. Total 780 eel stomachs were analyzed, of which 287 (37%) were empty. The weight of food content (2.1% of eel body mass) increased proportional with eel body mass. Chironomid larvae, Ephemeroptera nymph, Simulium larvae and Gammarus pulix were the dominating food taxa, followed by Trichoptera larvae. Size of prey were independent on length of eel.

Comprehensively understanding the spatial ecology and habitat preferences of endangered species is essential for population restoration and conservation. We investigated the home range and movement of the endangered Amur soft-shell turtle (Pelodiscus maackii) in the Ussuri River, Heilongjiang Province, Northeastern China. The study involved tracking 19 Amur soft-shell turtles from late June to mid-October, 2022, resulting in complete and partial home range size data for eight subadults and two adults, respectively. The primary analysis focused on eight subadults, and the models that best described daily movement were identified. We also explored the potential factors influencing home range size. Mean movement rate ranged from 39.18 ± 20.04 m to 72.45 ± 29.36 m and was positively correlated with the linear home range and water temperature. The most insightful home range estimate was a 95% kernel density estimate, employing likelihood cross-validation smoothing with constraints set to the river boundaries. The mean home range size was 1.02 ha and was not correlated with body size. Subadults tended to establish well-defined home ranges over time; whereas, defining home ranges for adults proved challenging. This research addresses a gap regarding the ecology of the Amur soft-shell turtle and provides a foundation for future conservation plans.

Quantifying different stocking rates effects in three study areas placed at Mediterranean forest (Cuenca, Spain) was conducted by applying a multiparametric soil quality index, SQI, developed in undisturbed forest soils (> 40 years). The main objective is to advance in the development and application of multiparametric indexes which allow for soil condition assessment. For it, is analyse the effectiveness of a multiparametric soil quality index (SQI) as an indicator livestock impacts on the soil in mediterranean forests. Control areas without stocking rates were compared in forest stands with different age (Thicket forest stand, < 30 years; High polewood forest stand, 30-60 years; Old growth forest stand, > 60 years); with various intensity grazing areas (permanent livestock passage: inactive sheepfold for 2-3 years, and active sheepfold; intermittent livestock passage: bare soil, pine stand, scrubland). The multiparametric soil quality index (SQI) applied sensitive to changes in forest ecosystems depending of different stocking rates. However, for even more preci-sion to assess cattle alteration, multiparameter index recalibrated, creating a new one; Soil Status Index by Livestock (SSIL). Correlation between quality ranges obtained with both indexes in dif-ferent study areas suggest that SSIL can be considered a livestock impact reference indicator in Mediterranean forest soils.

Obesity is invariably accompanied by autonomic dysfunction, although data in pediatric populations are conflicting. We conducted a systematic review and meta-analysis of 12 studies (totaling 1102 participants) comparing obese and normal-weight subjects (5-18 years of age), defined as body mass index &gt;95th or &lt;85th percentile, respectively. Using a random-effects model, we report the standardized mean differences (SMD) of sympathetic and vagal indices, calculated by heart rate variability. Autonomic dysfunction was present in the obese group, based on the average SMD in the standard deviation of sinus intervals (at -0.5340), and on the ratio of low (LF) to high (HF) frequency spectra (at 0.5735). There was no difference in sympathetic activity, but the heterogeneity among the relevant studies weakens this result. SMD in HF (at 0.5876), in the root mean square of successive differences between intervals (at -0.6333), and in the number of times successive intervals exceeded 50ms divided by the total number of intervals (at -0.5867) indicated lower vagal activity in the obesity group. Thus, autonomic dysfunction is present in obese children and adolescents, attributed to lower vagal activity. Further studies on autonomic function are needed in various pediatric cohorts, placing emphasis on sympathetic activity.

Stress can reduce the accuracy of radiation therapy owing to muscle contraction and changes in breathing patterns. This study aimed to predict stress in patients before each treatment session using artificial intelligence (AI) from biological signals to enhance treatment accuracy. We measured 123 stress cases in 41 patients and calculated stress scores by analyzing seven stress-related features derived from heart rate variability obtained from photoplethysmography. The study analyzed stress score distribution and observed its trend changes throughout the treatment. Before-treatment information was used to predict changes in stress features during treatment. AI models included both non-pre-trained (Decision Tree, Random Forest, Support Vector Machine, Long Short-Term Memory (LSTM), Transformer) and pre-trained (ChatGPT) models. The performance was evaluated using 10-fold cross-validation, exact match ratio, accuracy, recall, precision, and F1 score. More than 90% of the patients experienced stress during radiation therapy. From all the classifications, LSTM and prompt engineering GPT4.0 had the highest accuracy (feature classification, LSTM: 0.703, GPT4.0: 0.659, stress classification: LSTM: 0.846, GPT4.0: 0.769). Our research pioneers the use of AI and biological signals for stress prediction in radiation therapy, potentially identifying patients needing psychological support, and suggesting methods to improve radiotherapy effectiveness through stress management.

Eryngium foetidum L., a biennial herb with diverse applications in food and traditional medicine, holds economic and pharmacological significance. Given its growing commercial interest, implementing biotechnological approaches like plant tissue culture is vital for sustainable propagation and metabolite production. In this study, we investigated the in vitro photoautotrophic potential of E. foetidum, examining growth, chlorophyll a fluorescence, photosynthetic pigments, and anatomical features under different sucrose concentrations and gas exchange rates. Acclimatization and survival rates of plants after ex vitro transfer were also assessed. E. foetidum exhibited robust growth in both photoautotrophic and photomixotrophic conditions, with natural ventilation significantly enhancing plant development. Chlorophyll a fluorescence and photosynthetic performance were influenced by sucrose and gas exchange, highlighting the importance of these factors in plant micropropagation. Moreover, the species demonstrated remarkable plasticity during acclimatization, with high survival rates and rapid inflorescence emission. The research provides valuable insights into optimizing in vitro cultivation conditions for E. foetidum, emphasizing the potential for large-scale clonal propagation and exploring secondary metabolites. The observed phenotypic plasticity underscores the adaptability of the species to diverse environments. These biotechnological strategies open avenues for future studies, including the application of elicitors for enhanced secondary metabolite production.

Temperature increases mediated through climate change threaten the survival of species. It is of foremost importance to engage citizens and future generations in understanding the mechanisms through which temperatures imposes its affect. For educators though this is not straight forward as tools for examined the impact of temperature over the lifetime of an animal are prohibitively expensive. At the same time environmental educators need guidance on the appropriate study systems to use with a balance between the species having an obvious response and ensuring the outcomes are ethical and sustainable. In our study, we created and tested a cost-effective experiment meant to be used for environmental education purposes. More specifically, we tested the sensitivity of the Painted lady butterfly Vanessa cardui to temperature variations using a homemade incubator. We describe the design of this experiment and report findings on survival rate, morphological variations, pupation time, and wingspan of adults across a range of biologically relevant temperatures. The information provided give educators options for testing a variety of hypotheses with regards to the impacts of temperature using an affordable and flexible set-up. Furthermore, the findings can be used by students to develop an understanding of the ramifications of the butterflies responses in an ecological context.

The mechanical behavior, microstructures as well as the crystallographic textures of the Ti57-Nb43 alloy were investigated on cylindrical specimens compressed at high temperatures, in the range of 700-1000 °C, and strain rates between 0.001 and 1.0 s-1. Hardening, followed by softening behaviours were observed as a function of strain, due to the occurrence of dynamic recrystallization/recovery in hot deformation. The modified five-parameter Voce type equation described well the stress-strain curves, but for the present alloy, it was also possible with only four parameters. A new two-variables polynomial function was employed on the four parameters that described well the flow curves as a direct function of temperature and strain rate. It permitted to reduce the number of parameters and had the predictive capacity for the flow stress at any temperature, strain, and strain rate in the investigated range. The crystallographic textures were similar at all temperatures with an increase of intensity from 900 °C. The textures could be characterized by a double &lt;100&gt; and &lt;111&gt; fiber and a unique component of (001)&lt;110&gt;, the latter inherited from the initial hot rolling texture. Viscoplastic polycrystal self-consistent deformation modeling reproduced the measured textures showing that dynamic recrystallization did not alter the development of the deformation texture, only increased it intensity.

Composite polymolecular separation membranes were prepared by combining multi-branched ZIF-L with high porosity electrospinning nanofibers PI. Meanwhile, PDA and PEI were introduced into the membrane in order to improve its adhesion. The new membrane is so called “PI@PDA@PEI/ZIF-L-4” composite membrane; compared with PI@PDA@PEI/ZIF-8 composite membrane, the new membrane’s filtration rates for heavy metal ions such as Cd2+, Cr3+ and Pb2+ were increased by 7.0%, 6.6% and 9.3%, respectively. Furthermore, the new membrane has a permeability up to 1,140.0 L·m−2·h−1·bar−1, and were presenting a very stable performances after four repeated uses. The separation mechanism of PI@PDA@PEI/ZIF-L composite membranes was further analyzed in order to provide a base support for producing separation membranes with high barrier rate and high flux.

Polyhedral Oligomeric Silsesquioxanes (POSS) are one of the smallest silica nano particles. The presence of ordered ladder-like or cage conformation in their structure leads to remarkable thermal stability far greater than that for a linear polysiloxane. Reinforcement with nano particles is one of the recently favored strategy used to enhance the performance of polymers materials. The cyclic silica structure present in POSS ensures excellent thermal and mechanical stability. As a reinforcing filler, POSS can enhance the resistance of polymer matrix against pyrolysis and corrosion. In this paper, amine-terminated POSS with ladder like structure was synthesized by condensation of hydrolyzed aminosilane coupling agent. The ladder-like structure of POSS severely limits the motion of the chain backbone. The absence of phase transition for the synthesized POSS was confirmed by differential scanning calorimetry (DSC). Thermogravimetric analysis (TGA) was utilized to evaluate the thermal stability of POSS. TGA data show that the ladder-like POSS reported here has high char yield of about 75 % in nitrogen atmosphere and 54% in air atmosphere at 800℃. The TGA derivative curves show that the degradation of ladder-like POSS involves multiple stages and that the degradation rate is directly proportional to the heating rate. With the aid of Fourier Transform Infrared Spectroscopy (FT-IR), the cyclic structure of Si-O-Si bonding was determined to be the predominant structure. By analyzing the FTIR spectra of the cured POSS residue obtained at the specified temperatures, the detailed degradation mechanism of POSS was inferred. From the FTIR spectra of the low temperature cured samples, it was shown that the terminal silanol group was eliminated below 400℃. Some intermediate polymeric siloxane structures were observed by means of FT-IR and they eventually transitioned into silica which is the final product of pyrolysis of POSS. The presence of the FTIR transmission peaks at 1000 and 1100 cm-1, due to asymmetrical vertical and horizontal stretching of Si-O-Si, were the key evidence for the inferred ladder-like structure of POSS.

Titanium oxynitride thin films are deposited by DC reactive magnetron sputtering. A pure titanium target is sputtered in a reactive atmosphere composed of argon, oxygen and nitrogen gases. The oxygen mass flow rate as well as that of the nitrogen gas are both pulsed during the deposition time using an independent and rectangular signal for each reactive gas. A constant pulsing period T = 45 s is applied for both reactive gases and a delay time  of 34 s between N2 and O2 injection times is set for all depositions. Oxygen and nitrogen duty cycles are systematically and independently changed from 0 to 100% of their pulsing period. From real time measurements of the Ti target potential and total sputtering pressure, it is shown that the reactive process alternates between oxidized, nitrided and elemental sputtering modes as a function of the oxygen and nitrogen injection times. The full poisoning of the Ti target surface by oxygen and/or nitrogen can be avoided for some given ranges of O2 and N2 duty cycles. Deposition rates of titanium oxynitride films are substantially enhanced and can be adjusted between that of pure Ti and TiN films with a gradual transition of their optical transmittance in the visible range. These results support that titanium oxynitride compounds exhibiting absorbent to transparent behaviors can be precisely sputter-deposited by means of a two reactive gases pulsing process.

The G-NUMEN array is the future gamma spectrometer of the NUMEN experiment (Nuclear Matrix Element for the Neutrinoless double beta decay), to be installed around the object point of the MAGNEX magnetic spectrometer at the INFN-LNS laboratory.. This project aims at exploring Double Charge Exchange (DCE) reactions in order to obtain crucial information about the neutrinoless double beta decay (0νββ). The primary objective of the G-NUMEN array is to detect the gamma rays emitted from the de-excitation of the excited states populated via DCE reactions with good energy resolution and detection efficiency, amidst a background composed of transitions from competingreaction channels with far higher cross-sections. To achieve this, the G-NUMEN signalswill be processed in coincidence with those generated by the detection of the reaction ejectiles in the MAGNEX Focal Plane Detector(FPD). Under the expected experimental conditions, G-NUMEN detectors will operate at high counting rates, of the order of hundreds of kHz per detector, while maintaining excellent energy and timing resolutions. The complete array will consist of over 100 LaBr3(Ce) scintillators. Initial tests have been conducted on the first detectors of the array, allowing for the determination of their performance at high rates.

The nature of the fracture and fragmentation process of concrete medium under blast loading is the transformation process of the medium from continuum to discontinuity, coupled with the significant rate correlation of concrete medium, its mechanical behavior presents a high degree of complexity. Problems such as grid distortion and even negative volume are often encountered in the finite element method and others when solving this problem, while the material point method can effectively avoid these problems. In addition, the widely used HJC concrete constitutive model does not take into account the segmented characteristics of the calculation function for dynamic increasing factor. Therefore, in this paper, firstly, the calculation function for dynamic increasing factor in the HJC concrete model is modified by SHPB experiment, and the improved HJC concrete model is proposed; secondly, the material point method simulation program is developed, and the improved HJC concrete model is embedded into the simulation program; finally, the simulation program is verified by numerical examples, and the results show that the developed simulation program can better simulate the fracture and fragmentation process of concrete medium under blast loading, especially the pulverization characteristics of the medium in the near zone of the load.

Machining titanium alloy (Ti6Al4V) used in orthopedic implants by conventional metal cutting processes is challenging due to excessive cutting forces, low surface integrity and tool wear. To overcome these difficulties and for ensuring high-quality products, various industries employ wire electrical discharge machining (WEDM) for precise machining of intricate shapes in titanium alloy. The objective is to make WEDM machining parameters as efficient as possible for machining the bio-compatible alloy Ti6Al4V using box-behnken design (BBD) and Non-dominated Sorting Genetic Algorithm II (NSGA II). A quadratic mathematical model is created to represent the productivity and the quality factor (MRR and surface roughness) in terms of varying input parameters, such as pulse active (Ton) time, pulse inactive (Toff) time, peak amplitude (A) current and applied servo (V) voltage. The established regression models and related prediction plots provide a reliable approach for predicting how the process variables affect the two responses viz MRR and SR. The effects of four process variables on both the responses were examined, and the findings revealed that the pulse duration and voltage has a major influence on the rate at which material is removed (MRR) whereas pulse duration influence quality (SR).  The trade-off between MRR and SR, when significant process factors are included emphasizes the need for a reliable multi-objective optimization method. The intelligent metaheuristic optimization method named non-dominated sorting genetic algorithm II (NSGA II) is utilized to provide pareto optimum solutions in order to achieve high material removal rate (MRR) and low surface roughness (SR).

Abstract: Background and Objectives: Holter-based late potentials (LPs) are useful for predicting lethal arrhythmias in organic cardiac diseases. Although Holter-based LPs exhibit diurnal varia-tion, no studies have evaluated the optimal timing of LP measurement over 24 h for predicting lethal arrhythmia that leads to sudden cardiac death. Thus, this study aimed to validate the most effective timing for Holter-based LP testing and to explore factors influencing diurnal variability of LP parameters. Materials and Methods: We retrospectively analyzed 126 patients with post-myocardial infarction (MI) status and 60 control participants who underwent high-resolution Holter electrocardiography. Among the 126 post-MI patients, 23 developed sustained ventricular tachycardia (VT) (the MI-VT group), while 103 did not (the MI-non-VT group) during the obser-vation period. Holter-based LPs were measured at 0:00, 4:00, 8:00, 12:00, 16:00, and 20:00, and heart rate variability analysis was simultaneously performed to investigate factors influencing diurnal variability of LP parameters. Results: Holter-based LP parameters showed diurnal variation with significant deterioration at night and improvement during the day. Assessment at the time with the longest duration of low-amplitude signals <40 μV in the filtered QRS complex terminus (LAS40) gave the highest receiver operating characteristics curve (area under the curve, 0.659) and the highest odds ratio (3.75; 95% confidence interval, 1.45–9.71; p=0.006) for predicting VT. In the multiple regression analysis, heart rate and noise were significant factors affecting LP parameters in the MI-VT and control groups. In the non-VT group, LP parameters were significantly influenced by noise and parasympathetic heart rate variability parameters such as logPNN50. Conclusions: For Holter-based LP measurements, test accuracy was higher when LP was measured at the time of the highest or worst value of LAS40. Changes in autonomic nervous system activity, including heart rate and noise levels, were factors influencing diurnal variability.

Cardiac damage has been attributed to SARS-Cov-2-related pathology contributing to increased risk of vascular events. Heart rate variability (HRV) is a parameter of functional neurocardiac integrity with low HRV constituting an independent predictor of cardiovascular mortality. Whether structural cardiac damage translates into neurocardiac dysfunction in patients infected with SARS-CoV-2 remains poorly understood. Hypothesized mechanisms of possible neurocardiac dysfunction in COVID-19 comprise direct systemic neuroinvasion of autonomic control centers, ascending virus propagation along cranial nerves and cardiac autonomic neuropathy. While the relationship between the autonomic nervous system and the cytokine cascade in general has been studied extensively, the interplay between the inflammatory response caused by SARS-CoV-2 and autonomic cardiovascular regulation remains largely unclear. We reviewed the current literature on the potential diagnostic and prognostic value of autonomic neurocardiac function assessment via analysis of HRV in patients with COVID-19. Furthermore, we discuss potential therapeutic targets of modulating neurocardiac function in this high-risk population.

In the Mediterranean Sea, there are two critical issues affecting marine benthic biocenosis: the sedimentation process and the increasing trend in marine biodiversity loss. These processes are very marked along the western side of Calabria coastline where siltation is one of the main reasons for the regression of Posidonia beds in the regional coastal waters. This research aims to investigate the relationships between the geomorphological features of the debris source areas, represented by fluvial basins, and the distribution of Posidonia meadows. So, a geomorphological study of the Tyrrhenian fluvial basins with area greater than 200 km2 and its correlation with the mapping of the meadows in the Calabria Tyrrhenian waters was carried out. Furthermore, in order to assess the increased level of burial in a Posidonia oceanica meadow and its health state over time, a program of Posidonia monitoring was undertaken between 2000 and 2010, in a test area located in the Marine Regional Park of Isca (Calabria, Southern Italy). The results of this survey highlighted that, at the beginning of the study period, for a silting rate of 4 - 5 cm/year, the meadow suffered a reduction of its photosynthetic ratio with a rate of mortality of 50%. In the following years, the siltation rate reach 12 cm/year in 2010 and the meadow began to disappear. Therefore, the marine pollution by sedimentation represents a serious factor for the regression of Posidonia oceanica meadows, enhancing the risk of a gradual loss in marine biodiversity.

The growing production and application of carbon-based nanomaterials (CNMs) represent possible risks for aquatic systems. However, the variety of CNMs with different physical and chemical properties, and different morphology complicated the understanding of their potential toxicity. This paper aims to evaluate and compare the toxic impact of four most common CNMs, namely multiwalled carbon nanotubes (CNTs), fullerene (C60), graphene (Gr), and graphene oxide (GrO) in marine microalgae Porphyridium purpureum. The microalgae cells were exposed to the CNMs for 96 h and measured by flow cytometry. Based on the obtained results, we determined no observed effect level (NOEL), calculated EC10 and EC50 concentrations for growth rate inhibition, esterase activity, membrane potential, and reactive oxygen species (ROS) generation changes for each tested CNMs. According to the sensitivity (growth rate inhibition) of P. purpureum, the used CNMs can be listed in following order: CNTs &gt; GrO &gt; Gr &gt; C60. The toxicity of CNTs was significantly higher than the toxic effect of the other used CNMs and only this sample caused increase of ROS generation in microalgae cells. This effect caused by trace metal residuals in CNTs and high affinity between particles and microalgae associated with the presence of exopolysaccharide coverage on P. purpureum cells.

Hyper duplex stainless steels, HDSS, are a new alloy group of duplex stainless steels with the highest corrosion resistance and mechanical properties among the existing modern stainless steel. Due to the addition of extra high content of alloying elements, e.g. Cr, Ni, Mo, etc., the crystallization behavior of the δ-ferrite from the liquid is of vital importance for controlling the steel properties. In this work, the effect of cooling rate (i.e. 4°C/min and 150 °C/min) on the growth process of δ-ferrite in S33207 during the solidification process was investigated using high-temperature confocal scanning laser microscopy (HT-CLSM) in combination with electron microscopes and thermodynamic calculation. The results showed that the solidification mode of S33207 steel was a ferrite-austenite type (FA mode). L→δ transformation occurred at a certain degree of supercooling, and merging occurred during the growth of δ phase dendrites. Similar microstructures were observed after solidification under these two different cooling rates. The variation of area fraction of δ ferrite at the free surface and temperature as well as time during solidification of S33207 steels were calculated at different cooling rates of 4 °C/min and 150 °C/min, respectively. The current HT-CLSM study provides in-situ experimental evidence to confirm that a low cooling rate favored the growth of primary δ-ferrite, whereas a high cooling rate favored the nucleation of primary δ-ferrite during the solidification process. The post-microstructure as well as composition evolution is also briefly investigated. This work shed light on the real time insights for the crystallization behavior of hyper duplex stainless steels during solidification, which aims to contribute to the process control of manufacturing of this steel grade.

An essential metric for determining the efficacy of tuberculosis (TB) control programs is the evaluation of TB treatment outcomes. Assessing treatment outcomes is fundamental to facilitating the End TB Strategy set target. Clinic records from 457 patients with DR-TB were examined for data collection while 101 patients were followed up prospectively. Data were analyzed using Stata version 17.0. The odds ratio and 95% confidence interval were calculated to check the association between variables. P ≤ 0.05 was con-sidered statistically significant. Of the 427 participants, 65.8% had successful treatment whilst 34.2% had unsuccessful TB treatment. A total of 61.2% and 39% of the HIV-positive and HIV-negative participants had a successful TB treatment whilst 66% and 34% of both HIV-negative and positive participants had unsuccessful TB treatment. From 101 patients that were followed up, smokers had longer treatment outcomes compared to non-smokers. In the study with HIV/TB co-infection, men predominated. HIV and tuberculosis co-infection made therapy difficult with unfavorable effects on TB management. The treatment success rate (65.8%) is lower than the WHO threshold standard with a high proportion of patients lost to follow-up. The co-infection of tuberculosis and HIV resulted in undesirable treatment outcomes. Strengthening TB surveillance and control is recommended.

Oxidative stress driven by several environmental and local airway factors associated with chronic obstructive bronchiolitis, a hallmark feature of COPD, plays a crucial role in the disease pathomechanism. Unbalance between oxidants and antioxidant defense mechanisms not only amplifies the local inflammatory processes, but it also worsens cardiovascular health and contribute to the COPD related cardiovascular dysfunctions and mortality. The current review summarizes recent developments in our understanding of different mechanisms contributing to oxidative stress and its countermeasures with special attention to those that link local and systemic processes. Major regulatory mechanisms orchestrating these pathways are also introduced together with some suggestions for further research on the field.

Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 liters per minute (LPM), respectively, compared to 4.05 and 3.82 LPM of the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability, particularly in high-motion environments.

Maximal heart rate (HRmax) is a widely used measure of cardiorespiratory fitness. Prediction of HRmax is an alternative to cardiopulmonary exercise testing (CPET), but its accuracy among endurance athletes (EA) requires evaluation. This study aimed to externally validate HRmax prediction models in the EA independently for running and cycling CPET. 4043 runners (age=33.58 (8.12) years; 83.53% males; BMI=23.66 (2.54) kg·m−2) and 1026 cyclists (age=36.88 (9.03) years; 89.67% males; BMI=24.04 (2.65) kg·m−2) underwent maximum CPET. Student t-test, mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) were applied to externally validate 8 running and 5 cycling HRmax equations. HRmax was 184.60 (9.79) beats·min−1 and 182.66 (10.28) beats·min−1 respectively for running and cycling, p=0.001. Measured and predicted HRmax differed significantly (p=0.001) for 9 of 13 (69.23%) models. HRmax was overestimated by 8 (61.54%) and underestimated by 5 (38.46%) formulae. Overestimated HRmax ranged 0.08-4.94 beats·min−1 and underestimated HRmax ranged 0.03-4.90 beats·min−1. MAE and RMSE were 0.18-4.94 beats·min−1 and 9.13-10.47, respectively. MAPE ranged 3.95-4.69%. Prediction models do not allow for accurate estimation of HRmax. HRmax was more often underestimated than overestimated. Predicted HRmax can be implemented for EA as a supplemental method but CPET is the preferable approach.

The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track virus genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be 18 September 2021 (95% highest posterior density (HPD) 04 August – 22 October 2021) and 1.435×10-3 (95% HPD = 1.021×10-3 – 1.869×10-3) substitution/site/year, respectively, whereas 03 November 2021 (95% highest posterior density (HPD) 26 September – 28 November 2021) and 1.074×10-3 (95% HPD = 6.444×10-4 – 1.586×10-3) substitution/site/year for BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study shed light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics the virus and, as a result, mitigate the impact of emerging variants on public health.

Omicron BA.2.75 may become the next globally dominant strain of COVID-19 in 2022. BA.2.75 sub-variant has acquired more mutations (9) in spike protein and other genes of SARS-CoV-2 than any other variant. Thus, its chemical composition and thermodynamic properties have changed comparing to earlier variants. In this paper Gibbs energy of binding and antigen-receptor binding rate is reported for the BA.2.75 variant. Gibbs energy of binding (driving force of binding) of Omicron BA.2.75 variant is more negative than that of the competing variants BA.2 and BA.5.

Currently, old-style personal medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time-consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patients discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin is used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real-time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors which brings a breakthrough in wearable sensing automation.

Ofloxacin is a highly efficient and widely used antibiotic drug. It is classified as a refractory pollutant due to its poor biodegradability. Consequently, it is commonly found in water sources, requiring efficient methods for its removal. Advanced Oxidation Processes (AOPs) offer efficient alternatives since those yield complete degradation not achieved in adsorption or membrane processes. Previous studies suggest ofloxacin degradation follows a pseudo-first or -second order processes, whereas for full removal of refractory pollutants – lower pseudo-orders are required. Monitoring the actual “pseudo-order” degradation kinetics of ofloxacin is needed to evaluate any proposed AOP process. This study presents a simple procedure to evaluate pseudo-orders of AOPs. Photolysis of 20 mM ofloxacin solutions follow pseudo-zero order kinetics, with half-life times (t_1/2) of approx. 60 min. TiO₂ heterogenous catalyst show to have no influence at low concentration (0.2 mg L^-1) but a significant reduction of half-life time (t_1/2 = 20 min) and increase in pseudo-order (0.8) is measured at 2.0 mg L^-1. Similar results are obtained with homogenous catalysis by 2.0 mg L^-1 H₂O₂. The combination of H₂O₂ and TiO₂ catalysts shows additional reduction in half-time life with increase in the pseudo-order to 1.2. The conclusions are (1) heterogenous and homogenous photocatalysis can effectively degrade ofloxacin, (2) combined photocatalysis yields higher pseudo-order, being less prone to achieve full removal, (3) analysis of specific pseudo-orders in AOPs of refractory pollutants helps to further elucidate the efficiency of the processes.

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

A new method is proposed to increase rejection in microfiltration by applying membrane oscillation using a new type of microfiltration membranes with slotted pores. The oscillations applied to the membrane surface result in reducing membrane fouling and increasing separation efficiency. An exact mathematical solution of the flow in the surrounding solution outside the oscillating membrane is developed. The oscillation results in appearance of the lift velocity, which moves oil particles away from the membrane. The latter results in both reducing membrane fouling and increasing oil droplets rejection. This developed model was supported by the experimental results for oil water separation in produced water treatment. It was proven that oil droplet concentration reduced notably in the permeate due to the membrane oscillation and that applied shear rate caused by the membrane oscillation is also reduce pore blockage. New generation of microfiltration membranes with slotted pores was used in the experiments.

The building sector is the largest consumer of energy and there are still major scientific challenges in this field. The façade, being the interface between the exterior and interior space, plays a key role in the energy efficiency of a building. In this context, this paper focuses on a ventilated bioclimatic wall for NZEB zero energy buildings. The objective of this study is to investigate an experimental set-up based on a Hot Box allowing characterizing the thermal performances of the ventilated wall. A specific ventilated prototype and an original thermal metrology has been developed. This paper presents the ventilated prototype, the experimental set-up and experimental results on the thermal performances of the ventilated wall. The influence of the air space thickness and the air flow rate on the thermal performances of the ventilated wall is studied.

(1) Background: Most hemodialysis patients may experience physiological and psychological stress. Exposure to nature has been previously reported to reduce the measures of psychological and physiological stress, and immune function. This study aimed to investigate psychological and physiological effects of integrated indirect forest therapy on chronic renal failure patients undergoing hemodialysis. (2) Methods: As a quasi-experiment, this study employed a nonequivalent control group, repeated measurements, and a non-synchronized design. A total of 54 participants were included: 26 and 28 in the experimental and control groups, respectively. During hemodialysis, five types of forest therapy stimuli (visual, auditory, olfactory, tactile, and motor) were applied 3 times per week for 4 weeks during 15-minute sessions. (3) Results: Positive but not negative emotion measures differed between the groups after the intervention. Fatigue and physiological stress levels were significantly reduced in the experimental group, whereas no significant difference was found between the groups on the measures of psychological stress. Activation of both the parasympathetic and sympathetic nervous systems was similar in both groups, as was the number of natural killer cells. (4) Conclusion: Integrated indirect forest therapy may help increase positive emotions and reduce fatigue and stress levels during hemodialysis in patients with chronic renal failure.

An empirical model for log yield from trees is established and applied in microeconomics of carbon storage in a boreal spruce estate. The transition from pulpwood to sawlogs is a smoother function of stem diameter in the empirical data, in comparison to literature values. Correspondingly, the value transition of trees along with increasing size is gentler. Due to price premium of sawlogs from clearcuttings, all economically feasible treatment schedules terminate in clearcutting. Best capital return rates are gained with two heavy thinnings from above before clearcutting. Present carbon emission prices allow moderate carbon storage increment if the increment is compensated by proportional carbon rent. Doubling the present carbon prices would allow strong carbon storage increments if compensated by carbon rent. Application of nonproportional carbon rent is proposed.

Music perception in foetuses has been explored under different theoretical paradigms such as habituation, categorical perception, sound preferences and recall. This study investigated the temporal dimension of music perception through the habituation and sensitization paradigm. Foetuses of 41 pregnant women, mean gestational age of 34.7 weeks (±2.4), were observed during ultrasound exams. Foetuses’ reaction to two different tempos (Allegro vs Adagio) and sources (internal vs external) of music stimuli was registered by heart rate variability (HR) and motor response according to the Assessment of Preterm Infants Behaviour scale (APIB) by its factors of movement (MOV) and organization (ORG). A folkloric lullaby, sung and played live with a stringed instrument by a musician, was presented in three stages that were compared to baseline: 1) slow tempo (Adagio), 2) fast tempo (Allegro); and 3) now sung by mother at slow tempo (Adagio). Exploratory analyses showed that all factors increased from baseline to first stage. HR and ORG varied significantly among stages, with HR being the strongest factor. MOV merely detected change from baseline to first stage. ORG decreased for Allegro but increased for maternal Adagio, while HR decreased to near baseline values. ANOVA-repeated measures with gestational age as covariate showed that all measures were sensitive to first music presentation (Adagio), although only HR and ORG differed among stages. Considering estimated marginal means, adjusted for gestational age, HR presented a sensitization pattern throughout stages, but ORG kept habituating to external source and increased to maternal Adagio, suggesting foetal discrimination by sound source. We conclude that foetuses showed different behavioural and physiological responses to external versus internal sound source and musical tempo. The combined use of a behavioural scale (APIB) and HR in foetuses proved to be a valid multidimensional instrument.

Objective: Application of ERA methods to investigate the atmospheric pollution and built environment factors influencing lung cancer incidence rate in Chinese women. Methods: Lung cancer incidence rate among Chinese women at 339 cancer registries were obtained from the China Cancer Registry Annual Report 2017, air quality and built environment data were obtained from the Greenpeace and China Construction Yearbook. After multiple covariates variables were eliminated, an exploratory regression analysis was performed using the world standardized population incidence rate as the dependent variable. Air quality and built environment factors as the independent variable. Results: Shandong Peninsula, Hebei and Liaoning are high incidence rate areas of female lung cancer in China, with significant regional aggregation. In addition to air quality factors such as industrial smoke emission data, the association between built environmental factors such as urbanization rate, development LUI, population density and greening coverage of built-up areas and female lung cancer incidence rate is statistically significant. Conclusion: In addition to air quality factors, urban spatial factors can also significantly affect respiratory health. The LUI is positively while urbanization rates and population density are negatively correlated with the incidence rate of lung cancer. The role of green space for respiratory health has not been proven. In addition, there is little relationship between income and health, and similar findings are found for indicators such as the public transportation and roads network.

In this research work, we present a mathematical model for novel coronavirus -19 (NCOVID-19) which is consisted on three different compartments susceptible, infected and recovered classes abbreviated as under convex incident rate involving and emigration rate. We first derive the formulation of the model. Also, we give some qualitative aspects for the model including existence of equilibriums and its stability results by using various tools of nonlinear analysis. Then by mean of nonstandard finite difference scheme (NSFD), we simulate the results against the data of Wuhan city for the sixty days. By means of simulation, we show how protection, exposure, emigration, death and cure rates affect the susceptible, infected and recovered population with the passage of time involving emigration. On the basis of simulation, we observe the dynamical behavior due to emigration of susceptible and infected classes or one of these two.

The expense of carbon sequestration in terms of capital return deficiency is investigated at estate level, in the case of a fertile boreal estate dominated by spruce forest. Thinnings from below result as a high expense of increased rotation age, thinnings from above as a small expense. The expense of increased timber stock is greater than any proportional carbon rent based on present carbon prices. Application of non-proportional carbon rent is proposed.

Amplitude growth rates of monochromatic gravity waves were estimated and compared from multiple instrument measurements carried out in Brazil. Wave dynamic parameters were obtained from sodium density profiles from lidar observations carried out in Sao Jose dos Campos (23°S, 46°W), while all-sky images of multiple airglow layers provided amplitudes and parameters of waves over Cachoeira Paulista (23°S, 45°W). Growth rates of gravity wave amplitudes from lidar and airglow imager data were consistent with dissipative wave behavior. Only a small amount of the observed wave events presented freely propagating behavior. Part of the observed waves presented saturated amplitude. The general saturated/damped behavior is consistent with diffusive filtering processes imposing limits to amplitude growth rates of the observed gravity waves.

Polychlorinated phenoxathiins (PCPTs), polychlorinated dibenzothiophenes (PCDTs), and polychlorinated thianthrenes (PCTAs) are sulfur analogues of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/DFs). Chlorothiophenols(CTPs) and chlorophenols (CPs) are key precursors to form PCTA/PT/DTs, which can form chloro(thio)phenoxy radical, sulfydryl/hydryl-substituted phenyl radical and (thio)phenoxyl diradicals. The available radical/radical PCTA/DT formation mechanism failed to explain the higher concentration of PCDTs than that of PCTAs under the pyrolysis or combustion conditions. Thus in this work, a detailed thermodynamics and kinetic calculations were carried out to investigate the pre-intermediates formation for PCTA/PT/DTs from radial/molecule coupling of 2-C(T)P with their key radical species. Our study found that the radial/molecule mechanism can thermodynamically and kinetically contribute to the gas-phase formation of PCTA/PT/DT/s. The S/C coupling modes to form thioether-(thio)enol intermediats are preferable over the O/C coupling modes to form ether-(thio)enol intermediats. Thus, although the radial/molecule coupling of chlorophenoxy radical with 2-C(T)P have no effect on the PCDD/PTs formation, the radial/molecule coupling of chlorothiophenoxy radical with 2-C(T)P play an important role in the PCDT/PT formation. Most importantly, the pre-PCDT intermediates formation pathways from the coupling of sulfydryl/hydryl-substituted phenyl radical with 2-C(T)P and the coupling of (thio)phenoxyl diradicals with 2-C(T)P are more favorable to pre-PCTA/PT intermediates formation pathways from the coupling of chlorothiophenoxy radical with 2-C(T)P, which can give reasonable explanation for the high PCDT-to-PCTA ratio in the environment.

This article evaluates the macroeconomic implications of commercial bank seigniorage, which emerges from the commercial banks’ power to create money in a fractional reserves regime. After evaluating the impact on aggregate output of commercial bank money relative to alternative exchange arrangements, the article identifies the determinants of commercial bank seigniorage and analyzes how equilibrium prices are determined in an economy where commercial banks extract seigniorage. The article also identifies the conditions under which commercial banks extract seigniorage, clarifies the relationship between seigniorage from commercial bank money creation and profits from pure financial intermediation, and shows how commercial bank seigniorage changes with different types of interbank payments settlement.

In this study, regression analyses were used to find a relationship between the rain gauge rainfall rate R and radar reflectivity Z for the urban catchment of the Służewiecki Stream in Warsaw, Poland. Rainfall totals for 18 events which were measured at two rainfall stations were used for these analyses. Various methods for determining calculational values of radar reflectivity in reference to specific rainfall cells with 1-km resolution within an event duration were applied. The influence of each of these methods on the Z-R relationship was analyzed. The correction coefficient for data from the SRI (Surface Rainfall Intensity) product was established, in which the values of rainfall rate are calculated based on parameters a and b determined by Marshall and Palmer. Relatively good agreement between measured and estimated rainfall totals for the analyzed events was obtained using the Z-R relationships as well as the correction coefficient determined in this study. Rainfall depths estimated from radar data for two selected events were used to simulate flow hydrographs in the catchment using the SWMM (Storm Water Management Model) hydrodynamic model. Different scenarios were applied to investigate the stream response to changes in rainfall depths, in which the data both for 2 existing as well as 64 virtual rain gauges assigned to appropriate rainfall cells in the catchment were included.

Excessive vibration of temporary grandstand owing to crowd has lateral rhythmic motions can cause human in panic, which attracted increasing attention in recent years. This paper focuses on experiments that a temporary grandstand occupied 20 participants is oscillated by a shaking table with a series of random waves and induced by crowd with rhythmic swaying motions at lateral direction, respectively. And then a series of vibration perception questionnaires about crowd reaction are obtained. Evaluation of annoying level derives in concept of degree of membership and annoying rate method is proposed, then the human health risk of vibration serviceability of temporary grandstand is assessed with acceleration vibration dose value(VDV). From these results it is clear that standing crowd is more tolerant to vibration than seated crowd. The measured vibrations generated by crowd activities on temporary grandstand can cause panic in crowd. New relationship between the annoyance rate and structural acceleration VDV at logarithmic coordinate is proposed. The findings of this study can be utilized to manage the vibration of temporary grandstand and assess the human health risk.

In this work modeling and analysis of an integrated opto-fluidic sensor, with a focus on achievement of single mode optical confinement and continuous flow of micro particles in the microfluidic channel for Lab-on-a Chip (LOC) sensing application is presented. This sensor consists of integrated optical waveguides, microfluidic channel among other integrated optical components. A continuous flow of micro particles in a narrow fluidic channel is achieved by maintaining the two sealed chambers at different temperatures and by maintaining a constant pressure of 1Pa at the centroid of narrow fluidic channel geometry. The analysis of silicon on insulator (SOI) integrated optical waveguide at an infrared wavelength of 1550nm for single mode sensing operation is presented. The optical loss is found to be 0.0005719dB/cm with an effective index of 2.2963. The model presented in this work can be effectively used to detect the nature of micro particles and continuous monitoring of pathological parameters for sensing applications.

This paper investigates the impact of price and real exchange rate volatility on Foreign Direct Investment (FDI) inflows in a panel of 10 Latin American and Caribbean countries, observed between 1990 and 2012. Both price and exchange rate volatility series are estimated through the Generalized Autoregressive Conditional Heteroscedasticity model (GARCH). Our results, obtained employing the Fixed Effects estimator, confirm the theory of hysteresis and option value, in so far it is found a statistically significant negative effect of exchange rate volatility on FDI. Price volatility, instead, turns out to be positive but insignificant. Moreover, we show that human capital and trade openness are key for attracting foreign capital. From the policy perspective, our analysis suggests the importance of stabilization policies as well as the one of government credibility in promoting trade openness and human capital formation.

People wore clothes for warmth, survival and necessity in modern life as well but in modern era, eco-friendliness, shorten production time, design, and intelligence also matters. The seamless bonding technology strengthen the bond between fabrics in textiles, while Grey relational analysis (GRA) can be applied to fabrics to measure the relationship between data series and determine the closeness of each data series. In this study, polyurethane prepolymer, 2-Hydroxyethyl acrylate (2-HEA) as an end-capping agent, and n-octyl acrylate (ODA) as a photo-initiator were used to synthesize dual-curing polyurethane hot-melt adhesive. The Taguchi quality engineering and grey relational analysis used to discuss the influence of different mole ratio of NCO:OH, and the effect of the molar ratio of addition of octyl decyl acrylate on the mechanical strength. The Fourier transform infrared spectroscopy (FTIR) results showed the termination of prepolymer's polymerization reaction and the C=O peak intensity at 1730 cm-1 indicating the efficient bonding to main chain. The high molecular weight polymer (20,000-30,000) described by advanced polymer chromatography (APC) confirmed the polyurethane polymer bonded with octyl decyl acrylate to achieve the photo-thermosetting effect. The thermogravimetric analysis (TGA) results showed the thermal decomposition temperature of polyurethane hot melt adhesive also increased and highest pyrolysis temperature (349.89℃) for the polyhydric alcohols. The high value of shear strength (34.94 kg/cm2) as well as peel strength (1.68 kg/cm) were also observed in dual-cure photo-thermosetting polyurethane hot melt adhesive. The signal-to-noise ratio also used to generate the grey relational degree. It was observed that the best parameter ration of NCO: OH was 4:1 with 5 moles of monomer. The Taguchi quality engineering method was used to find the parameters of single quality optimization, and then the grey relation calculation was used to obtain the parameter combination of multi-quality optimization for thermosetting polyurethane hot melt adhesive. The study aims to meet the requirements of seamless bonding in textile factories and optimized experimental parameter design by setting target values can effectively increase production speed, reduce processing time and costs as well.

A series of defective ZSM-5 zeolites (~300 nm, SiO₂/Al₂O₃ ratio of 55, 100, 400 and 950) were intentionally prepared and systematically studied by XRD, SEM, ²⁹Si MAS NMR, argon physisorption, NH₃-TPD and FT-IR technologies. The nature, the amount and the accessibility of the acid sites of defective ZSM-5 zeolites are greatly different from reported ZSM-5 zeolites with perfect crystal structure. The co-existed strong Brønsted acid sites (Si(OH)Al) and weak Brønsted acid sites (hydroxyl nests) over defective ZSM-5 zeolites might lead to unique catalytic function. Zn(C₂H₅)₂ was grafted onto defective ZSM-5 zeolites through chemical liquid deposition (CLD) method. Interestingly, FT-IR spectroscopy studies find that Zn(C₂H₅)₂ was preferentially grafted on the hydroxyl nests with weak acidity rather than the Si(OH)Al groups with strong acidity over different defective ZSM-5 zeolites. Particularly, home-built operando dual beam FTIR-MS was applied to study the catalytic performance of Zn species locating at different sites of defective ZSM-5 zeolites under n-hexane transformation. Results show that Zn species grafted over hydroxyl nests obtain better dehydrogenation performance than Zn species over framework aluminum. This study provides guidance for the rational design of highly efficient alkane dehydrogenative aromatization catalysts.

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network’s sustainable operation and growth. In this paper, we consider a WPT-MEC network wit user cooperation to migrate the double near-far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Rigorous mathematical analysis shows that our algorithm achieves O(1/V),O(V) trade-off between computation rate and queues stability. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability.

This paper introduces a three-layer system, proposing a comprehensive model of granular mixture transport over a mobile sloped bed in a steady flow. This system, consisting of the bottom, contact and upper zones, provides complete, continuous sediment velocity and concentration vertical profiles. The model considers gravity’s effect on sediment transport in the bottom (dense) layer, a crucial factor often overlooked in previous studies. The induced movement in the mobile sublayer of the dense layer causes an increase in velocity at the upper boundary of this layer, significantly affecting the vertical distributions of velocity and concentration above this layer. We described the interactions between fractions and an intensive sediment mixing and sorting process in the contact layer. The proposed shear variation, with an increase in its maximum value at the bed, adds to the novelty of our approach. The data sets used for the model’s validation cover various conditions, including slopes, grain diameters, densities and grain mobility conditions, from incipient motion to a fully mobilised bed. This extensive validation process instils confidence in the theoretical description and its applicability to real-world scenarios.

It is challenging to precisely measure the slow interseismic crustal deformation rate from SAR data. The long-wavelength orbital errors, owing to the uncertainties in satellite orbit vectors, commonly exist in SAR interferograms, which degrade the precision of the InSAR products and become the main barrier of extracting interseismic tectonic deformation. In this study, we propose a novel temporal network orbital correction method that is able to isolate the far-fault tectonic deformation from the mixed long-wavelength signals based on its spatio-temporal characteristic. The proposed approach is straightforward in methodology but could effectively separate the subtle tectonic deformation from glaring orbital errors without ancillary data. Both synthetic data and real Sentinel-1 SAR images are used to validate the reliability and effectiveness of this method. The derived InSAR velocity fields clearly present the predominant left-lateral strike-slip motions of the Tuosuo Lake segment of the Kunlun fault in western China. The fault-parallel velocity differences of 5-6 mm/yr across the fault between areas ~50 km away from the fault trace are addressed. The proposed method presents significantly different performance from traditional quadratic approximate method in the far-field. Through the implementation of the proposed method, the RMSE between the LOSGPS and our derived descending InSAR LOS is reduced to less than one-third of the previous study, suggesting its potential to enhance the availability of InSAR technology for interseismic crustal deformation measurement.

This paper studies the maximum reliability of multi-hop relay UAV, in which UAV provides wireless services for remote users without direct communication link on the ground as a coded cooperative relay. In this paper, the analytical expressions of total power loss and total bit error rate are derived as reliability measures. Firstly, based on the environmental statistical parameters, a LOS probability model is proposed. Then the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss, and subject to the maximum allowable path loss con-straints, transmission power allocation constraints and UAV height and elevation constraints. At the same time the total path loss is minimized to achieve maximum ground communication cov-erage. However, the formulated joint optimization problem is non-convex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different, and using the mobility of UAVs can improve the reliability of communication performance.

Ethanolic extracts of propolis collected from Apis mellifera hives have been shown to reduce Nosema ceranae infection in honey bee workers. This study aimed to determine the propolis concentration that optimizes the control of N. ceranae infection in honey bee workers. Newly emerged worker bees were individually fed 2 μL of 50% sucrose solution (w/v) containing 105 N. ceranae spores per bee, and treated with 0%, 25%, 50%, and 75% ethanolic extracted propolis in 50% sucrose solution (v/v). The treated bees were maintained in cages at 34 ± 2˚C and 55 ± 5% RH throughout the study. The number of dead bees was counted and removed from the cages daily for 30 days to generate survival curves. The number of N. ceranae spores per bee (infectivity) was quantified using a hemocytometer, and the number of infected bees per hundred bees (infection rate) was determined. The survival rates of N. ceranae infected bees fed 25%, 50%, and 75% propolis (NP1, NP2, NP3, respectively) were significantly higher than those of infected bees not fed propolis (NO). Furthermore, the infectivity and infection rates of NP2 and NP3 groups were significantly lower than those of the NO group. The infectivity and infection rates of the NP1 group were not different than those of the NO group. Our data suggest that 50% and 75% propolis extracts are appropriate concentrations for treating N. ceranae infection in honey bees. Ethanolic extracted propolis can be an alternative treatment for N. ceranae infection in Western honey bees.

Wild rocket plants were cultivated in a plastic greenhouse into two independent sectors, one for soil-bound (SbS) cultivation and another equipped for soilless (ScS) cultivation systems. In both SbS and ScS, the crop was subjected to treatments consisting in a high-compared to a low-input fertilization program (HF and LF treatment, respectively). Water use efficiency (WUE) and partial factor productivity (PFP) for nutrients (N, P, K, Ca and Mg for ScS, and N for SbS) were measured. Rocket leaves, separated for cultivation system and fertilization program, collected at different cuts during the growing cycle, were cold stored at 10 °C until 16 d. At each sampling day (at harvest and during the storage), sensory parameters, respiration rate, dry matter, colour, electrolyte leakage, antioxidant activity, total phenols, total chlorophyll and ammonia content were evaluated. In ScS, the PFP for all nutrients supplied as fertilizers showed a significant increase with the LF treatment, with values higher than 30 % for N, K and Ca. As for the postharvest performance, rocket leaves cultivated in ScS showed better qualitative traits than those cultivated in SbS, as suggested by the lower values of ammonia content and electrolyte leakage recorded at the end of storage in samples grown in ScS. Moreover, in ScS, data showed a lower membrane damage in LF than HF rocket leaves. Finally, regarding total chlorophyll content, even if no effect of treatments was recorded in SbS, rocket cultivated in ScS, showed a better retention of this parameter applying LF than HF treatment. In addition to this, a PLS model (R2 = 0.7) able to predict the cultivation system, using as variables total chlorophyll content no-destructively measured, was implemented.

Infectious diseases are one of the leading causes of death among children under the age of 5 with 60% of those deaths preventable with prophylaxis. Vaccinations prove to be the most succesfull and effective public health tool of infectious dieases preventions. Poland first implemented a program of mandatory childhood vaccinations in 1960. The program is updated each year and provides parents with a clear schedule to follow. The program includes vaccinations against 12 diseases (tuberculosis, hepatitis B, rotaviral disease, diphtheria, pertussis, tetanus, Haemophilus influenzae, Streptococcus pneumoniae, poliomyelitis, measles, mumps and rubella) mandatory for all children and funded by the government. It also includes recommended vaccinations which costs are covered by a patient. Immunization rates are monitored nationwide and reported to the Chief Sanitary Inspector. With recent outbreaks of infectious diseases among young children the obligation lies on the government to provide childhood immunizations, and largely on the physicians to provide reliable education about infectious diseases in children and safety of vaccinations to the parents.

Grain supply is the lifeblood of a country, and the stability of the supply chain is a crucial prerequisite for ensuring national grain security. This paper draws on the definition of resilience in physics and economics. It takes supply chain fracture resilience, impact resilience, and synergy resilience as the secondary indicators. It constructs a comprehensive evaluation indicator system of the grain supply chain resilience, measures the resilience indicator of China's grain supply chain from 1996 to 2021, and analyzes the role of supply, cost, exchange rate, and other external shocks in influencing the resilience of China's grain supply chain on this basis. The study found that the overall level of China's total grain supply chain resilience has been growing year by year and can be divided into three stages: low-level stabilization stage, continuous growth stage, and high-level stabilization stage. Grain supply chain fracture resilience has been growing steadily, grain supply chain impact resilience fluctuation is more obvious, and grain supply chain synergy resilience has been changing more gently. In the inquiry of the impact of external shocks on the resilience of China's grain supply chain, it was found that world grain exports and the RMB exchange rate have a significant positive impact on China's grain supply chain resilience level, while the international oil price has a significant negative impact. Based on this, the paper puts forward suggestions for ensuring stable production and supply in the grain market, improving the structure of foreign trade in grain, and actively coping with international commodity price shocks.

More and more people quantify their sleep using wearables and are getting obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and even can lead to negative daytime consequences. Acknowledging these facts, we here aim to extend previous findings [] in a new sample of 136 self-reported poor sleepers by implementing optimization procedures to minimize erroneous classification when ambulatory sensing sleep. Firstly, here, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. Results reveal high overall accuracy for the new model for ECG (86.3 %, κ=.79), H10 (84.4%, κ=.76), and VS (84.2%, κ=.75) with intended improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication which can be considered a prerequisite for use also in high age groups and/or with common disorders. Further improving and validating sleep stage classification algorithms with affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.